Competition of crystal field splitting and Hund's rule coupling in two-orbital magnetic metal-insulator transitions

Competition of crystal field splitting and Hund's rule coupling in magnetic metal-insulator transitions of half-filled two-orbital Hubbard model is investigated by multi-orbital slave-boson mean field theory. We show that with the increase of Coulomb correlation, the system firstly transits from a paramagnetic (PM) metal to a {\it N\'{e}el} antiferromagnetic (AFM) Mott insulator, or a nonmagnetic orbital insulator, depending on the competition of crystal field splitting and the Hund's rule coupling. The different AFM Mott insulator, PM metal and orbital insulating phase are none, partially and fully orbital polarized, respectively. For a small $J_{H}$ and a finite crystal field, the orbital insulator is robust. Although the system is nonmagnetic, the phase boundary of the orbital insulator transition obviously shifts to the small $U$ regime after the magnetic correlations is taken into account. These results demonstrate that large crystal field splitting favors the formation of the orbital insulating phase, while large Hund's rule coupling tends to destroy it, driving the low-spin to high-spin transition.Comment: 4 pages, 4 figure

State Transition Algorithm

In terms of the concepts of state and state transition, a new heuristic random search algorithm named state transition algorithm is proposed. For continuous function optimization problems, four special transformation operators called rotation, translation, expansion and axesion are designed. Adjusting measures of the transformations are mainly studied to keep the balance of exploration and exploitation. Convergence analysis is also discussed about the algorithm based on random search theory. In the meanwhile, to strengthen the search ability in high dimensional space, communication strategy is introduced into the basic algorithm and intermittent exchange is presented to prevent premature convergence. Finally, experiments are carried out for the algorithms. With 10 common benchmark unconstrained continuous functions used to test the performance, the results show that state transition algorithms are promising algorithms due to their good global search capability and convergence property when compared with some popular algorithms.Comment: 18 pages, 28 figure

Reduction in Northward Incursions of the South Asian Monsoon Since ~1400 AD Inferred from a Mt. Everest Ice Core

A highly resolved Mt. Everest ice core reveals a decrease in marine and increase in continental air masses related to relatively high summer surface pressure over Mongolia, and reduction in northward incursions of the summer South Asian monsoon since similar to 1400 AD. Previously published proxy records from lower sites south of the Himalayas indicate strengthening of the monsoon since this time. These regional differences are consistent with a south north seesaw in convective activity in the Asian monsoon region, and reflect a southward shift in the mean summer position of the monsoon trough since similar to 1400 AD. The change in monsoonal circulation at 1400 AD is synchronous with a reduction in solar irradiance and the onset of the LIA. This demonstrates a hemispheric scale circulation reorganization at this time, and the potential for future large shifts in monsoonal circulation

Use of observing system simulation experiments in the United States

Author Posting. © American Meteorological Society, 2020. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Bulletin of the American Meteorological Society 101(8), (2020): E1427-E1438, https://doi.org/10.1175/BAMS-D-19-0155.1.The NOAA Science Advisory Board appointed a task force to prepare a white paper on the use of observing system simulation experiments (OSSEs). Considering the importance and timeliness of this topic and based on this white paper, here we briefly review the use of OSSEs in the United States, discuss their values and limitations, and develop five recommendations for moving forward: national coordination of relevant research efforts, acceleration of OSSE development for Earth system models, consideration of the potential impact on OSSEs of deficiencies in the current data assimilation and prediction system, innovative and new applications of OSSEs, and extension of OSSEs to societal impacts. OSSEs can be complemented by calculations of forecast sensitivity to observations, which simultaneously evaluate the impact of different observation types in a forecast model system

Mechanisms of Degradation and Identification of Connectivity and Erosion Hotspots

The context of processes and characteristics of soil erosion and land degradation in Mediterranean lands is outlined. The concept of connectivity is explained. The remainder of the chapter demonstrates development of methods of mapping, analysis and modelling of connectivity to produce a spatial framework for development of strategies of use of vegetation to reduce soil erosion and land degradation. The approach is applied in a range of typical land use types and at a hierarchy of scale from land unit to catchment. Patterns of connectivity and factors influencing the location and intensity of processes are identified, including the influence of topography, structures such as agricultural terraces and check dams, and past land uses. Functioning of connectivity pathways in various rainstorms is assessed. Modes of terrace construction and extent of maintenance, as well as presence of tracks and steep gradients are found to be of importance. A method of connectivity modelling that incorporates effects of structure and vegetation was developed and has been widely applied subsequently

Empiricism Without the Senses: How the Instrument Replaced the Eye

On receiving news of Galileo’s observations of the four satellites of Jupiter and the rugged face of the moon through his newly invented perspicillum, Kepler in great excitement exclaimed: Therefore let Galileo take his stand by Kepler’s side. Let the former observe the moon with his face turned skyward, while the latter studies the sun by looking down at a screen (lest the lens injure his eyes). Let each employ his own device, and from this partnership may there some day arise an absolutely perfect theory of the distances. This Hollywood-like scene of the two astronomers marching hand in hand toward the dawn of a new scientific era was no attempt by Kepler to appropriate Galileo’s success or to diminish the novelty of the telescope. On the contrary, Kepler repeatedly asserted how short sighted he was in misjudging the potential for astronomical observations inherent in lenses, and how radically Galileo’s instrument transformed the science of astronomy. It was a deep sense of recognition that beyond their different scientific temperaments and projects, they shared a common agenda of a new mode of empirical engagement with the phenomenal world: the instrument. For Kepler and Galileo, empirical investigation was no longer a direct engagement with nature, but an essentially mediated endeavor. The new instruments were not to assist the human senses, but to replace them

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types

Selection of Optimal Quantile Protein Biomarkers Based on Cell-Level Immunohistochemistry Data

BACKGROUND: Protein biomarkers of cancer progression and response to therapy are increasingly important for improving personalized medicine. Advanced quantitative pathology platforms enable measurement of protein expression in tissues at the single-cell level. However, this rich quantitative cell-by-cell biomarker information is most often not exploited. Instead, it is reduced to a single mean across the cells of interest or converted into a simple proportion of binary biomarker-positive or -negative cells. RESULTS: We investigated the utility of retaining all quantitative information at the single-cell level by considering the values of the quantile function (inverse of the cumulative distribution function) estimated from a sample of cell signal intensity levels in a tumor tissue. An algorithm was developed for selecting optimal cutoffs for dichotomizing cell signal intensity distribution quantiles as predictors of continuous, categorical or survival outcomes. The proposed algorithm was used to select optimal quantile biomarkers of breast cancer progression based on cancer cells\u27 cell signal intensity levels of nuclear protein Ki-67, Proliferating cell nuclear antigen, Programmed cell death 1 ligand 2, and Progesterone receptor. The performance of the resulting optimal quantile biomarkers was validated and compared to the standard cancer compartment mean signal intensity markers using an independent external validation cohort. For Ki-67, the optimal quantile biomarker was also compared to established biomarkers based on percentages of Ki67-positive cells. For proteins significantly associated with PFS in the external validation cohort, the optimal quantile biomarkers yielded either larger or similar effect size (hazard ratio for progression-free survival) as compared to cancer compartment mean signal intensity biomarkers. CONCLUSION: The optimal quantile protein biomarkers yield generally improved prognostic value as compared to the standard protein expression markers. The proposed methodology has a broad application to single-cell data from genomics, transcriptomics, proteomics, or metabolomics studies at the single cell level